Sentence-Level Relation Extraction.
Early research efforts [1]}, [2]}, [3]} train RE models from scratch based on lexicon-level features.
Recent work has shifted to fine-tuning pretrained language models (PLMs; [4]}, [5]}) resulting in better performance.
For example,
BERT-MTB [6]} continually finetunes the PLM with a matching-the-blanks objective that decides whether two sentences share the same entity.
SpanBERT [7]} pretrains a masked language model on random contiguous spans to learn span-boundaries and predict the entire masked span.
LUKE [8]} extends the PLM's vocabulary with entities from Wikipedia and proposes an entity-aware self-attention mechanism.
K-Adapter [9]} fixes the parameters of the PLM and uses feature adapters to infuse factual and linguistic knowledge.
Despite their effectiveness, most existing work on sentence-level RE exploits the entity information and context within only an individual instance, while we propose to globally capture the semantic information from the whole dataset to augment the relation representations.
Our model can be flexibly plugged into existing RE models and improve their effectiveness without increasing the time complexity.
